© 2004 Prentice-Hall, Inc.Chap 1-1 Basic Business Statistics (9 th Edition) Chapter 1 Introduction and Data Collection

© 2004 Prentice-Hall, Inc. Chap 1-1

Basic Business Statistics (9th Edition)

Chapter 1Introduction and Data

Collection


Chapter Topics

Why a Manager Needs to Know About Statistics

The Growth and Development of Modern Statistics

Some Important Definitions Descriptive Versus Inferential Statistics


Chapter Topics

Why Data Are Needed Types of Data and Their Sources Design of Survey Research Types of Survey Sampling Methods Evaluating Survey Worthiness Types of Survey Errors

(continued)


What is Statistics?

Science of gathering, analyzing, interpreting, and presenting data

Branch of mathematics Measurement taken on a sample Type of distribution being used to

analyze data Making decision under uncertainty


Why a Manager Needs to Know About Statistics

To Know How to Properly Present Information

To Know How to Draw Conclusions about Populations Based on Sample Information

To Know How to Improve Processes To Know How to Obtain Reliable

Forecasts


The Growth and Development of Modern

Statistics

Needs of government to collect data on its citizenry

The development of the mathematics of probability theory

The advent of the computer


Some Important Definitions

A Population (Universe) is the Whole Collection of Things Under Consideration

A Sample is a Portion of the Population Selected for Analysis

Census — gathering data from the entire population

A Parameter is a Summary Measure Computed to Describe a Characteristic of the Population

A Statistic is a Summary Measure Computed to Describe a Characteristic of the Sample


Population and Sample

Population Sample

Use parameters to summarize features

Use statistics to summarize features

Inference on the population from the sample


Statistical Methods

Descriptive Statistics Collecting, presenting, and characterizing

data using data gathered on a group to describe or

reach conclusions about that same group only

Inferential Statistics Drawing conclusions and/or making decisions

concerning a population based only on sample data


Descriptive Statistics

Collect Data E.g., Survey

Present Data E.g., Tables and graphs

Characterize Data E.g., Sample Mean =

iX

n


Inferential Statistics

Estimation E.g. Estimate the

population mean weight using the sample mean weight

Hypothesis Testing E.g. Test the claim that

the population mean weight value is 120 pounds

Drawing conclusions and/or making decisions concerning a population based on sample results.


Why We Need Data

To Provide Input to a Survey To Provide Input to a Study To Measure Performance of Ongoing

Service or Production Process To Evaluate Conformance to Standards To Assist in Formulating Alternative

Courses of Action To Satisfy Curiosity


Data Sources

Observation

Experimentation

Survey

Print or Electronic

Data Sources


Design of Survey Research

Choose an Appropriate Mode of Response Reliable primary modes

Personal interview Telephone interview Mail survey E-mail

Less reliable self-selection modes (not appropriate for making inferences about the population)

Television survey Internet survey Printed survey in newspapers and magazines Product or service questionnaires

p.9



Identify Broad Categories List complete and non-overlapping

categories that reflect the theme Formulate Accurate Questions

Clear and unambiguous questions use clear operational definitions – universally accepted definitions

Test the Survey Pilot test on a small group of participants to

assess clarity and length

(continued)



Write a Cover Letter State the goal and purpose of the survey Explain the importance of a response Provide assurance of respondent anonymity Offer incentive gift for respondent

participation

(continued)


Types of Data

Categorical(Qualitative)

Discrete Continuous

Numerical(Quantitative)

Data


Type of Data

Categorical random variables yield categorical responses E.g. Are you married? Yes or No

Numerical random variables yield numerical responses Discrete random variables yield numerical

response that arise from a counting process E.g. How many cars do you own? 3 cars

Continuous random variables yield numerical responses that arise from a measuring process

E.g. What is your weight? 130 pounds

(continued)


Levels of Measurement and Types of Measurement

Scales

Nominal Scale – distinct categories in which no ordering is implied E.g. Type of stocks invested: growth, income, other and none

Ordinal Scale – distinct categories in which ordering is implied E.g. Student grades: A, B, C, D or F

Interval Scale – an ordered scale in which the difference between the measurements does not involve a true zero point E.g. Temperature in degrees Celsius

Ratio Scale – an ordered scale in which the difference between the measurements involves a true zero point E.g. Weight in pounds


Reasons for Drawing a Sample

Less Time Consuming Than a Census Less Costly to Administer Than a Census Less Cumbersome and More Practical to

Administer Than a Census of the Population


Types of Sampling Methods

Quota

Samples

Non-Probability Samples

Judgement Chunk

Probability Samples

Simple Random

Systematic

Stratified

Cluster

Convenience


Probability Sampling

Subjects of the Sample are Chosen Based on Known Probabilities

Probability Samples

Simple Random

Systematic Stratified Cluster


Simple Random Samples

Every Individual or Item from the Frame Has an Equal Chance of Being Selected

Selection May Be With Replacement or Without Replacement

One May Use Table of Random Numbers or Computer Random Number Generators to Obtain Samples


Systematic Samples Decide on Sample Size: n Divide Frame of N individuals into

Groups of k Individuals: k=N/n Randomly Select One Individual from

the 1st Group Select Every k-th Individual Thereafter

N = 64

n = 8

k = 8

First Group


Stratified Samples Population Divided into 2 or More

Groups According to Some Common Characteristic

Simple Random Sample Selected from Each Group

The Two or More Samples are Combined into One


Cluster Samples

Population Divided into Several “Clusters,” Each Representative of the Population

A Random Sampling of Clusters is Taken All Items in the Selected Clusters are

Studied Population divided into 4 clusters

Randomly selected 2 clusters


Advantages and Disadvantages

Simple Random Sample & Systematic Sample Simple to use May not be a good representation of the

population’s underlying characteristics Stratified Sample

Ensures representation of individuals across the entire population

Cluster Sample More cost effective Less efficient (need larger sample to acquire the

same level of precision)


Evaluating Survey Worthiness

What is the Purpose of the Survey? Is the Survey Based on a Probability

Sample? Coverage Error – Appropriate Frame Nonresponse Error – Follow up Measurement Error – Good Questions

Elicit Good Responses Sampling Error – Always Exists


Types of Survey Errors

Coverage Error

Nonresponse Error

Sampling Error

Measurement Error

Excluded from frame

Follow up on nonresponses

Chance differences from sample to sample

Bad Question!


Chapter Summary Addressed Why a Manager Needs to

Know about Statistics Discussed the Growth and Development

of Modern Statistics Addressed the Notion of Descriptive

Versus Inferential Statistics Discussed the Importance of Data


Chapter Summary

Defined and Described the Different Types of Data and Sources

Discussed the Design of Surveys Discussed Types of Survey Sampling

Methods Evaluated Survey Worthiness Described Different Types of Survey

Errors

(continued)

Documents

© 2004 Prentice-Hall, Inc.Chap 1-1 Basic Business Statistics (9 th Edition) Chapter 1 Introduction and Data Collection